ReviewSemantic SLAM: A comprehensive survey of methods and applications
Keywords
1. Introduction
Fig. 1. Hierarchical structure of semantic SLAM systems.
Fig. 2. History and evolution of SLAM over the years from 1986 to 2025.
Fig. 3. Illustration of the number of papers published between 2015 to 2025 in the areas of semantic SLAM, indoor scene understanding, and outdoor scene understanding, highlighting the evolving research focus and growing interest in these fields over time.
Digital Science (2024).Fig. 4. Pie chart illustrating the distribution of research areas in semantic SLAM, where 24.4% of the research is in automation and 25.6% in remote sensing.
- •Comprehensive coverage: We provide the most up-to-date and systematic review of semantic SLAM methods, filling the gap left by earlier surveys that focused only on geometric SLAM or isolated aspects of semantics.
- •Dataset and metrics analysis: Unlike prior reviews, we analyze commonly used datasets and performance metrics in semantic SLAM, highlighting their strengths, limitations, and suitability for different scenarios.
- •Taxonomy and categorization: We propose a structured taxonomy that organizes semantic SLAM approaches by sensor type, environmental context, and object representation, offering a clearer perspective than existing fragmented overviews.
- •Reproducibility and benchmarking: We reproduce selected results from prior studies, which have not been addressed in previous surveys, to provide insights into reproducibility and reliability of semantic SLAM methods.
- •Future outlook: We identify unresolved challenges, such as scalability, robustness in dynamic environments, and real-time semantic integration, and suggest research directions that go beyond the scope of earlier surveys.
Fig. 5. Schematic diagram of the overall paper structure discussing various sections and their sub-sections.
2. Survey methodology
2.1. Search strategy
- •Paper Inclusion Criteria
- –Review articles, proceedings, and journals
- –Articles written in English
- –Articles published between 2015 and 2025
- –Articles focusing on semantic SLAM, indoor scene understanding, and outdoor scene understanding
- –Articles with SCI, SCIE, and Conference Proceedings citation index
- –Articles focusing on research areas like robotics, computer science, and engineering
- –
- •Paper Exclusion Criteria
- –Each article will be counted only once, even if it appears in multiple digital libraries
- –Articles that are not peer-reviewed
- –Studies that do not have experimental results
- –Papers not directly related to the primary focus of semantic SLAM and robotics
- –Duplicate studies from other sources not listed
- –Papers not listed in Q1, with exceptions for significant contributions
- –
Fig. 6. Flowchart illustrating the systematic paper selection process employed in this survey, where duplicates were removed, followed by a screening of titles and abstracts for relevance. Articles were assessed against strict inclusion and exclusion criteria, focusing on semantic SLAM.
2.2. Paper selection results
Fig. 7. Bar graph illustrating the number of publications per year in the Web of Science database from 2015 to 2025.
Fig. 8. A pie chart illustrating the distribution of the papers reviewed in our survey, showing that 84% of them were journal articles.
3. Background and fundamentals about semantic SLAM
Fig. 9. Integration of processes, sensors, and algorithms in SLAM systems.
Fig. 10. General semantic SLAM framework.
3.1. Sensors
Table 1. Advantages and disadvantages of different semantic SLAM sensor approaches.
| Type | Advantages | Disadvantages |
|---|---|---|
| Monocular semantic SLAM | Low cost Lightweight High portability | Scale ambiguity Limited depth perception Vulnerable in dynamic scenes |
| Stereo semantic SLAM | Improved depth accuracy Enhanced environmental understanding Robustness | Higher computational cost Increased hardware complexity Higher cost |
| RGB-D semantic SLAM | Rich sensory data Ease of scene reconstruction Handles dynamic environments well | Limited range Sensitivity to lighting conditions Higher energy consumption |
| 3D-Lidar based semantic SLAM | High accuracy Robust to lighting conditions Effective in large-scale environments | High cost Complexity Limited by weather conditions |
| Multi-modal semantic SLAM | Comprehensive understanding Robustness Improved accuracy | High computational cost Increased system complexity Costly |
| Incremental semantic SLAM | Continuous mapping Adaptability Resource efficiency | Complex algorithm design Drift Limited scalability |
3.1.1. Monocular semantic SLAM
Fig. 11. Monocular semantic SLAM parallel threads.
3.1.2. Stereo semantic SLAM
Fig. 12. Stereo semantic SLAM parallel threads.
3.1.3. RGB-D semantic SLAM
Fig. 13. RBG-D semantic SLAM parallel threads.
3.1.4. 3D LiDAR-based semantic SLAM
Fig. 14. 3-D LiDar-based semantic SLAM parallel threads.
3.1.5. Multi-modal semantic SLAM
Fig. 15. Multi-modal semantic SLAM parallel threads.
3.1.6. Incremental semantic SLAM
Fig. 16. Incremental semantic SLAM parallel threads.
3.2. Environment
3.2.1. Semantic SLAM for dynamic indoor environment
3.2.2. Semantic SLAM for dynamic outdoor environment
Table 2. Dynamic semantic SLAM method comparison.
| Reference | Dynamic object detection | Environment suitability | Strengths | Weaknesses |
|---|---|---|---|---|
| Gupta et al., 2015, Qi et al., 2025, Xu et al., 2019 and Yang, Ran, Wang, Lu, and Chen (2022) | Combines Mask R-CNN instance segmentation with residual-based motion filtering | Indoor | Dense object-level maps; tracks moving objects; reconstructs background | Low frame rate; COCO-dependent; needs GPU; not for large/outdoor scenes |
| Bescos, Campos, Tardós and Neira (2021) and Ying et al. (2023) | Classifies ORB features as static/dynamic; combines geometry and semantic cues | Outdoor | Real-time object-aware SLAM; full 6-DoF for camera/objects; good for driving | Sparse maps; ignores segmentation delay; needs stereo/RGB-D; accuracy depends on segmentation |
| Ge, Zhang, Wang, Coleman, and Kerr (2023) and Gonzalez, Marchand, Kacete, and Royan (2022) | Groups points by semantic class; motion modeled with mechanical constraints | Outdoor | Robust tracking for objects (e.g., cars); accurate camera pose with dynamic objects | No dense mapping; needs accurate segmentation/joint models; not for cluttered/indoor settings |
| Wang, Wu, Li and Yu (2024) and Judd and Gammell (2024) | Scene flow clustering and multilabel RANSAC; no semantics required | Indoor and Outdoor | Unsupervised; tracks multiple motions without semantics; occlusion-tolerant | High computation; not real-time; sparse maps; lacks object-level detail; needs stereo/depth sensors |
3.2.3. Approaches to 3D object representation
Fig. 17. A detailed illustration of different types of 3D object representations commonly used in semantic SLAM.
Table 3. 3D object representations for semantic SLAM in scene understanding.
| Reference | Data type | Characteristics | Advantages | Challenges |
|---|---|---|---|---|
| Chen, Shao et al., 2022, Choudhary et al., 2017, Li, Zhou et al., 2024, Nie et al., 2020, Peng, Zhao et al., 2024, Wen et al., 2021, Xie et al., 2022, Xu et al., 2020, Yang et al., 2020 and Zhu, Xiao and Fan (2025) | Descriptors | Describe geometric or topological characteristics Capture shape, surface, and texture information | Object recognition Shape similarity Efficient 3D processing | Deformable shape handling Large-scale scalability |
| Gong et al., 2021, Huang et al., 2023, Jung et al., 2025, Liu, Mi et al., 2021, Sandstrom et al., 2023, Wang, Tian et al., 2025, Yang, Chen et al., 2023, You et al., 2022 and Ying and Li (2023) | Projections | Convert 3D objects into 2D grids | Retains key shape characteristics | Information loss in dense tasks |
| Choi et al., 2015, Jin et al., 2020, Mascaro et al., 2022, Popovic et al., 2021, Rosinol et al., 2023, Rosu et al., 2020, Wang, Tian, Liu, 2025 and Yan, Wang, He, Chang, and Zhuang (2020) | Volumetric (voxel/octree) | Grid-based 3D space modeling | Simple, structured encoding | High memory cost Poor resolution scalability |
| Cheng et al., 2023, Cheng et al., 2021, Dang et al., 2019, Deng et al., 2020, Kuang et al., 2022, Muthu et al., 2020, Yan et al., 2022 and Zhang, Zhang, Jin and Yi (2022) | RGBD | Combines color and depth info (2.5D) | Cost-effective, accurate pose and scene understanding | Struggles with noisy/incomplete data |
| An et al., 2022, He et al., 2024, Huang et al., 2024, Islam et al., 2024, Shi, Zha et al., 2020, Zheng et al., 2025 and Yang, Ye, Zhang, Wang, and Qiu (2024) | Multi-view geometry | Combine multiple 2D images for 3D reconstruction | Reduces noise and occlusion Tolerant to lighting issues | Sensitive to calibration errors Not ideal for dynamic scenes |
| Bescos, Cadena et al., 2021, Kong et al., 2023, Li, Guo et al., 2025 and Ruan, Zang, Zhang, and Huang (2023) | Neural field | MLPs represent object surfaces | Compact, watertight, coherent representation | Complex temporal modeling Requires large datasets |
| Han and Yang, 2023, Peng, Xu et al., 2024, Tian et al., 2024, Tschopp et al., 2021 and Wei and Wang (2018) | Super quadrics (SQ) | Compact 3D shape abstraction from point clouds | Efficient representation with shape fidelity | Training requires large datasets Sensitive to temporal variance |
| Cho et al., 2020, Isele et al., 2021, Li, Fu et al., 2024, Li et al., 2022, Pan et al., 2024, Vishnyakov et al., 2021 and Zhang, Huo, Huang, and Liu (2025) | Point cloud | Unstructured 3D points without topology | Flexible and detailed geometry | Hard to model globally Calibration sensitivity |
| Arshad and Kim, 2024, Duan et al., 2022, Fernandez-Cortizas et al., 2024, Liu, Yuan et al., 2024, Qian et al., 2022 and Zhang, Zhang, Liu, Naixue Xiong and Li (2024) | Graphs | Nodes as vertices; edges encode relationships | Scalable and expressive for both local/global tasks | High complexity Hard to visualize large graphs |
| Herb et al., 2021, Rosu et al., 2020 and Wang, Zhang and Li (2020) | Meshes | Polygons and vertices define surface geometry | Preserves structure for segmentation and matching | Irregular structure hampers DL integration Sensitive to resolution and noise |
4. Datasets
4.1. TUM RGB-D dataset
Table 4. TUM RGB-D dataset sequences.
| Sequence | Description | Image size | Frame rate |
|---|---|---|---|
| fr3_walking_xyz | Walking sequence with significant translational motion in x, y, z directions | 640 480 pixels | 30 Hz |
| fr3_walking_static | Static scene with minimal motion | 640 480 pixels | 30 Hz |
| fr3_walking_rpy | Walking sequence with rotational motion in roll, pitch, yaw | 640 480 pixels | 30 Hz |
| fr3_walking_half | Half walking sequence with moderate motion | 640 480 pixels | 30 Hz |
4.2. KITTI dataset
Table 5. KITTI dataset sequences.
| Sequence | Description | Image size | Frame rate |
|---|---|---|---|
| KITTI 00 | Urban environment with moderate traffic | 1242 375 pixels | 10 Hz |
| KITTI 01 | Highway environment with high-speed motion | 1242 375 pixels | 10 Hz |
| KITTI 02 | Urban environment with dynamic objects | 1242 375 pixels | 10 Hz |
| KITTI 03 | Rural environment with varying terrains | 1242 375 pixels | 10 Hz |
| KITTI 04 | Urban environment with sharp turns and occlusions | 1242 375 pixels | 10 Hz |
4.3. BONN dataset
4.4. A1 and Jackal
4.5. uHumans2
4.6. CarSim
4.7. openLORIS
4.8. BeVIS (Indoor parking dataset)
4.9. Scenesv2
4.10. Freiburg cars
4.11. Redwood-OS chairs
Table 6. Prominent open-source frameworks for semantic SLAM and their key contributions.
| Framework | Key features | Applications | Reference |
|---|---|---|---|
| ORB-SLAM3 (with Semantic Extensions) | Multi-camera, stereo, and inertial SLAM; semantic object integration via Mask R-CNN | Robust semantic SLAM across diverse environments | Campos, Elvira, Rodr’iguez, Montiel, and Tardós (2020) |
| Kimera | Real-time metric-semantic mapping; 3D scene graphs; integrates visual–inertial odometry | Robot navigation, semantic scene understanding | Rosinol, Abate, Chang and Carlone (2019) |
| DROID-SLAM | End-to-end deep learning-based dense SLAM; robust to dynamics; lightweight | Visual odometry, dynamic scene tracking | Teed and Deng (2021) |
| SemanticFusion | Combines CNN-based semantic segmentation with ElasticFusion for dense maps | Indoor semantic mapping | McCormac, Handa, Davison, and Leutenegger (2016) |
| MaskFusion | Object-aware SLAM; fuses instance segmentation with 3D reconstruction | Augmented reality, dynamic object mapping | Rünz and Agapito (2018) |
| Co-Fusion | Multi-object segmentation and tracking in real-time; extends ElasticFusion | Dynamic SLAM with moving objects | Rünz and Agapito (2017) |
| Semantic voxblox | Incremental volumetric mapping with semantic fusion | Long-term mapping, mobile robotics | Palazzolo, Behley, Lottes, Giguère, and Stachniss (2019b) |
| PanopticFusion | Panoptic segmentation integrated into dense SLAM pipeline | Scene understanding, semantic mapping | Narita, Seno, Ishikawa, and Kaji (2019) |
| DS-SLAM | Dynamic Semantic SLAM using deep learning for segmentation and static/dynamic separation | Robust localization in dynamic scenes | Yu et al. (2018b) |
| OpenVSLAM (with semantics) | Versatile, modular SLAM with support for multiple camera models; extensible with semantics | Lightweight robotics, reproducible experiments | Sumikura, Shibuya, and Sakurada (2019) |
| MonoScene-SLAM (emerging) | Combines monocular SLAM with 3D scene completion and semantic priors | 3D reconstruction from monocular cameras | Cao and de Charette (2021) |
5. Advancements in semantic SLAM for scene understanding
5.1. Key approaches in indoor scene understanding
5.2. Key approaches in outdoor scene understanding
Fig. 18. Timeline diagram for the most commonly known semantic SLAM techniques.
5.3. Emerging trends in semantic SLAM
Table 7. Benchmark comparison of core techniques and characteristics of semantic visual SLAM systems.
| Reference | Method | Technique | Network | Sensors used | Public datasets | Indoor | Outdoor | Dynamic | Available |
|---|---|---|---|---|---|---|---|---|---|
| Fan, Zhang, Tang, Liu, and Han (2022) | Blitz SLAM | ORB SLAM2 | BlitzNet, ResNet50 | RGBD Camera | TUM RGBD | ||||
| Lin et al. (2024) | DPL-SLAM | ORB SLAM3 | YOLOv5-s | Intel D435i | TUM RGB-D, KITTI | ||||
| Lv et al. (2024a) | MOLO-SLAM | ORB SLAM2 | Mask-RCNN | LiDAR, Kinect, Realsense | TUM, KITTI | ||||
| Qin et al. (2024) | RSO-SLAM | ORB SLAM2 | YOLOv5-seg, LiteFlowNet2 | ZED2i Stereo | TUM, BONN, KITTI | ||||
| Zhao et al. (2022) | KSF-SLAM | ORB SLAM2 | SegNet | ZED stereo | TUM RGB-D, KITTI | ||||
| Liu and Miura (2021b) | RDS-SLAM | ORB SLAM3 | – | KinectV2 | TUM RGBD | ||||
| Ran, Yuan, Zhang, Tang et al. (2021) and Xiong et al. (2023) | RS-SLAM | ORB SLAM2 | PSPNet | RGB-D | TUM | ||||
| Abate et al., 2024, Zheng et al., 2024 and Zhang, Song et al. (2025) | Kimera2 | Pose Graph | 3D Dynamic Scene Graph | LiDAR, RGBD, IMU | A1, Jackal | ||||
| Cheng et al. (2023) | SG-SLAM | ORB SLAM2 | NCNN | RGBD Camera | TUM, BONN | ||||
| Wu et al. (2020) | EAO-SLAM | ORB SLAM2 | YOLOv3 | RGBD Camera | TUM, Scenes V2 | ||||
| Li, Zou et al. (2023) and Luo, Rao, and Wu (2023) | FD-SLAM | ORB SLAM3 | Fast-SCNN, Deepfillv2 | RGBD Camera | TUM RGB-D | ||||
| Wang, Runz et al. (2021) | DSP-SLAM | ORB SLAM2 | – | LiDAR, Stereo, RGBD | KITTI3D, Redwood Chairs | ||||
| Wen et al. (2023) | Dynamic SLAM | ORB SLAM2 | SegNet | RGBD, IMU, LiDAR | KITTI | ||||
| Cao et al. (2022) and Esparza and Flores (2022) | STDyn-SLAM | ORB SLAM2 | SegNet + VGG16 | ZED | KITTI | ||||
| Yang, Gao et al. (2025) | OpenGS-SLAM | GS + Semantic Voting | SAM1.0, MobileSAMv2 | RGBD Camera | Replica, TUM | ||||
| Li, Liu et al. (2024) | SGS-SLAM | Semantic GS | CNN + Semantic Loss | RGBD Camera | ScanNet, TUM | ||||
| Wang, Lu et al. (2025) | SG-SLAM | Semantic Graph | SegNet4D | LiDAR | KITTI, MulRAN | ||||
| Li, Hao et al. (2025) | Hier-SLAM++ | Hier GS + Semantic Loss | CLIP, SAM | RGB-D, Mono | Replica, TUM | ||||
| Laina et al. (2025) | FindAnything | VL Semantic SLAM | CLIP, DINO, SAM | RGB Camera | Replica |
6. Applications of semantic SLAM
6.1. Intelligent/precision agriculture
6.1.1. Sensor modalities in agricultural SLAM
6.2. Intelligent industry and warehousing
6.3. Autonomous driving
7. Practical challenges and deployment
7.1. Computational requirements
7.2. Robustness in real-world environments
7.3. Scalability and long-term mapping
7.4. From research to commercial products
8. Performance metrics used in semantic SLAM
Fig. 19. Performance metrics for evaluation of semantic SLAM in scene understanding.
8.1. Tracking metrics
8.2. Semantic mapping metrics
8.3. Geometric SLAM metrics
8.3.1. Absolute Trajectory Error (ATE)
8.3.2. Relative Pose Error (RPE)
8.3.3. Root Mean Square Error (RMSE)
8.3.4. Statistical measures
Table 8. Benchmark comparison of performance metrics using the TUM RGBD datasets.
| Reference | Accuracy (%) | ATE (m) | RPEt (m) | RPEr (deg) | IoU (%) |
|---|---|---|---|---|---|
| Fan et al. (2022) | 0.0159 | 0.0182 | 0.5785 | ||
| Wu, Guo et al. (2022) | 0.0546 | 0.0315 | 0.7417 | ||
| Cheng et al. (2023) | 0.0175 | 0.02196 | 0.5611 | ||
| Qian et al. (2021) | 92.19 | 0.0429 | |||
| Wu et al. (2020) | 81.75 | ||||
| Bavle, De La Puente, How, and Campoy (2020) | 0.0365 |
Table 9. Benchmark comparison of performance metrics using the Bonn datasets.
| Reference | ATE (m) | RPEt (m) | RPEr (deg) |
|---|---|---|---|
| He et al. (2023) | 0.0245 | 0.1878 | 14.2961 |
| Singh et al. (2022) | 0.0620 | 0.0690 | |
| Jiang, Xu, Li, Feng, and Zhang (2024) | 0.1230 | ||
| Wu, Guo et al. (2022) | 0.0890 | ||
| Cheng et al. (2023) | 0.0644 | ||
| Li, Guo et al. (2025) | 0.0290 |
Table 10. Benchmark comparison of performance metrics using the KITTI datasets.
| Reference | Accuracy (%) | ATE (m) | RPEt (m) | RPEr (deg) |
|---|---|---|---|---|
| Qin et al. (2024) | 2.3136 | 0.0072 | 0.0020 | |
| Lv et al. (2024a) | 3.5343 | 1.8074 | ||
| Wang, Li, Shen and Cai (2020) | 1.3267 | 0.4815 | ||
| Esparza and Flores (2022) | 1.4493 | 0.0233 | ||
| Chen, Liu et al. (2022) | 80.82 | 4.606 | ||
| Singh et al. (2022) | 0.87 |
8.4. Replication of results from open-source papers
8.4.1. System specifications
- •Processor: AMD Ryzen 9 3950X 16-Core Processor with 32 threads, operating at a base clock speed of 2.2 GHz and a maximum clock speed of 3.5 GHz.
- •GPU: NVIDIA GeForce RTX 2080 Ti with 11 GB of VRAM, supporting CUDA version 12.1.
- •Operating System: Ubuntu 16.04.
- •Robotics Framework: ROS Melodic.
- •TUM RGB-D dataset
- •KITTI dataset
8.4.2. Results
- •TUM dataset: The following evaluation results were obtained using the TUM RGB-D indoor dataset, with a focus on the sequences fr3_walking_xyz, fr3_walking_static, fr3_walking_rpy, and fr3_walking_half, as presented in Tables 11, 12, and 13, respectively. Key performance metrics such as ATE, RMSE, RPEt, and RPER, are used to highlight the effectiveness of integrating semantic information in enhancing SLAM accuracy in dynamic environments. The integration of semantic information in dynamic SLAM systems significantly enhances their performance compared to traditional methods like ORB-SLAM2. This improvement is evident in the superior results across various metrics and sequences from the TUM dataset. For instance, SG-SLAM achieves an ATE of 0.019 m in the fr3_walking_xyz sequence, vastly outperforming ORB-SLAM2’s 0.693 m. Additionally, SG-SLAM’s RPEt for the same sequence is 0.022 m, which is significantly better than ORB-SLAM2’s 0.475 m.
- •KITTI dataset: The KITTI dataset, particularly in outdoor environments, further highlights the benefits of semantic SLAM. For instance, VDO-SLAM with Mask R-CNN records an ATE of 1.2 m in the KITTI 00 sequence, compared to ORB-SLAM2’s 1.3 m. Furthermore, VDO-SLAM achieves a RPEt of 0.06 m in the same sequence, compared to ORB-SLAM2’s 0.04 m. These results emphasize the importance of semantic data integration, which enhances scene understanding by allowing the system to distinguish between different objects and dynamic elements. Tables 14, 15, and 16 present a comprehensive evaluation of the performance of different SLAM systems integrated with semantic information, including Dyna-SLAM and VDO-SLAM, both of which use Mask R-CNN for semantic integration, compared to the traditional ORB-SLAM2 system, which does not employ semantic data. The evaluation is based on KITTI outdoor dataset sequences, focusing on key metrics such as ATE, RPEt, and RPEr.
Table 11. Evaluation of semantic SLAM systems on TUM datasets using ATE performance metric.
| Sequence | SG-SLAM (NCNN) (Cheng et al., 2023) | Dyna-SLAM (Mask R-CNN) (Bescos et al., 2018) | DS-SLAM (SegNet) (Yu et al., 2018a) | YOLO-SLAM (darknet19-yolov3) (Wu, Guo et al., 2022) | RDS-SLAM (Mask R-CNN) (Liu & Miura, 2021b) | RDS-SLAM (Segnet) (Liu & Miura, 2021b) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Results | Original | Ours | Original | Ours | Original | Ours | Original | Ours | Original | Ours | Original | Ours |
| fr3_walking_xyz | 0.0152 | 0.019 | 0.015 | 0.016 | 0.024 | 0.023 | 0.014 | 0.013 | 0.021 | 0.021 | 0.057 | 0.056 |
| fr3_walking_static | 0.007 | 0.008 | 0.007 | 0.006 | 0.008 | 0.0078 | 0.007 | 0.006 | 0.081 | 0.078 | 0.02 | 0.02 |
| fr3_walking_rpy | 0.032 | 0.034 | 0.136 | 0.135 | 0.443 | 0.444 | 0.216 | 0.223 | 0.146 | 0.145 | 0.16 | 0.159 |
| fr3_walking_half | 0.026 | 0.023 | 0.029 | 0.029 | 0.03 | 0.03 | 0.028 | 0.028 | 0.025 | 0.030 | 0.08 | 0.08 |
Table 12. Evaluation of semantic SLAM systems on TUM dataset using RPEt performance metric.
| Sequence | SG-SLAM (NCNN) (Cheng et al., 2023) | Dyna-SLAM (Mask R-CNN) (Bescos et al., 2018) | DS-SLAM (SegNet) (Yu et al., 2018a) | YOLO-SLAM (darknet19-yolov3) (Wu, Guo et al., 2022) | RDS-SLAM (Mask R-CNN) (Liu & Miura, 2021b) | RDS-SLAM (Segnet) (Liu & Miura, 2021b) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Results | Original | Ours | Original | Ours | Original | Ours | Original | Ours | Original | Ours | Original | Ours |
| fr3_walking_xyz | 0.0194 | 0.022 | 0.021 | 0.022 | 0.033 | 0.033 | 0.019 | 0.019 | 0.028 | 0.028 | 0.042 | 0.043 |
| fr3_walking_static | 0.010 | 0.013 | 0.008 | 0.009 | 0.0102 | 0.011 | 0.009 | 0.0087 | 0.041 | 0.042 | 0.022 | 0.022 |
| fr3_walking_rpy | 0.045 | 0.074 | 0.044 | 0.045 | 0.150 | 0.15 | 0.093 | 0.092 | 0.111 | 0.111 | 0.132 | 0.132 |
| fr3_walking_half | 0.027 | 0.032 | 0.028 | 0.028 | 0.029 | 0.029 | 0.026 | 0.027 | 0.028 | 0.027 | 0.048 | 0.051 |
Table 13. Evaluation of semantic SLAM systems on TUM dataset using RPEr performance metric.
| Sequence | SG-SLAM (NCNN) (Cheng et al., 2023) | Dyna-SLAM (Mask R-CNN) (Bescos et al., 2018) | DS-SLAM (SegNet) (Yu et al., 2018a) | YOLO-SLAM (darknet19-yolov3) (Wu, Guo et al., 2022) | RDS-SLAM (Mask R-CNN) (Liu & Miura, 2021b) | RDS-SLAM (Segnet) (Liu & Miura, 2021b) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Results | Original | Ours | Original | Ours | Original | Ours | Original | Ours | Original | Ours | Original | Ours |
| fr3_walking_xyz | 0.504 | 0.504 | 0.628 | 0.627 | 0.826 | 0.834 | 0.598 | 0.588 | 0.723 | 0.028 | 0.922 | 0.919 |
| fr3_walking_static | 0.267 | 0.270 | 0.261 | 0.271 | 0.269 | 0.269 | 0.262 | 0.342 | 1.168 | 0.042 | 0.494 | 0.540 |
| fr3_walking_rpy | 0.956 | 0.957 | 0.989 | 1.002 | 3.012 | 3.000 | 1.823 | 1.823 | 9.319 | 0.111 | 13.170 | 13.210 |
| fr3_walking_half | 0.811 | 0.812 | 0.784 | 0.776 | 0.814 | 0.812 | 0.753 | 0.752 | 0.821 | 0.027 | 1.876 | 1.874 |
Table 14. Evaluation of semantic SLAM systems on KITTI dataset using ATE performance metric.
| Seq | Dyna-SLAM (Mask R-CNN) (Bescos et al., 2018) | VDO-SLAM (Mask R-CNN) (Zhang, Henein, Mahony, & Ila, 2020) | ||
|---|---|---|---|---|
| Results | Original | Ours | Original | Ours |
| KITTI 00 | 1.4 | 1.2 | 1.2 | 1.2 |
| KITTI 01 | 9.4 | 10.1 | 8.9 | 8.7 |
| KITTI 02 | 6.7 | 7.1 | 5.4 | 5.7 |
| KITTI 03 | 0.6 | 0.6 | 0.6 | 0.6 |
| KITTI 04 | 0.2 | 0.3 | 0.2 | 0.2 |
Table 15. Evaluation of semantic SLAM systems on KITTI dataset using RPEt performance metric.
| Seq | Dyna-SLAM (Mask R-CNN) (Bescos et al., 2018) | VDO-SLAM (Mask R-CNN) (Zhang et al., 2020) | ||
|---|---|---|---|---|
| Results | Original | Ours | Original | Ours |
| KITTI 00 | 0.04 | 0.03 | 0.067 | 0.072 |
| KITTI 01 | 0.05 | 0.04 | 0.044 | 0.044 |
| KITTI 02 | 0.04 | 0.05 | 0.021 | 0.020 |
| KITTI 03 | 0.06 | 0.04 | 0.03 | 0.04 |
| KITTI 04 | 0.07 | 0.06 | 0.05 | 0.05 |
Table 16. Evaluation of semantic SLAM systems on KITTI datasets using RPEr performance metric.
| Seq | Dyna-SLAM (Mask R-CNN) (Bescos et al., 2018) | VDO-SLAM (Mask R-CNN) (Zhang et al., 2020) | ||
|---|---|---|---|---|
| Results | Original | Ours | Original | Ours |
| KITTI 00 | 0.06 | 0.05 | 0.07 | 0.072 |
| KITTI 01 | 0.04 | 0.03 | 0.012 | 0.003 |
| KITTI 02 | 0.03 | 0.03 | 0.04 | 0.04 |
| KITTI 03 | 0.04 | 0.04 | 0.08 | 0.078 |
| KITTI 04 | 0.06 | 0.05 | 0.11 | 0.10 |
8.4.3. Benchmarking against ORB-SLAM2
Table 17. ATE comparison on TUM RGB-D dataset.
| Sequence | ORB-SLAM2 (m) | SG-SLAM (m) |
|---|---|---|
| fr3_walking_xyz | 0.693 | 0.019 |
| fr3_walking_static | 0.392 | 0.008 |
| fr3_walking_rpy | 1.022 | 0.034 |
Table 18. RPEt comparison on TUM RGB-D dataset.
| Sequence | ORB-SLAM2 (m/frame) | SG-SLAM (m/frame) |
|---|---|---|
| fr3_walking_xyz | 0.475 | 0.022 |
| fr3_walking_static | 0.361 | 0.013 |
| fr3_walking_rpy | 0.451 | 0.074 |
Table 19. ATE comparison on KITTI dataset.
| Sequence | ORB-SLAM2 (m) | SG-SLAM (m) |
|---|---|---|
| KITTI 00 | 1.3 | 1.2 |
| KITTI 01 | 10.4 | 10.1 |
| KITTI 02 | 5.7 | 7.1 |
- •Accuracy Improvements: Semantic SLAM systems consistently outperformed ORB-SLAM2 across both datasets. SG-SLAM demonstrated a 97.3% improvement in ATE for the fr3_walking_xyz sequence, while DynaSLAM showed marginal gains for challenging KITTI sequences, such as KITTI 01.
- •Dynamic Object Handling: Semantic methods significantly reduced errors in sequences with high dynamic content. For example, SG-SLAM reduced RPEt in the fr3_walking_xyz sequence by over 95% compared to ORB-SLAM2.
- •Efficiency Trade-Offs: Semantic systems, while more accurate, often require higher computational resources. RDS-SLAM, however, provided a balance between accuracy and efficiency, achieving notable reductions in processing time compared to ORB-SLAM2.
8.4.4. Processing time
Table 20. Average processing time per frame (ms).
| Systems | Average processing time per frame (ms) |
|---|---|
| ORB-SLAM2 | 59.26 |
| SG-SLAM (NCNN) | 65.71 |
| YOLO-SLAM (darknet19-yolov3) | 696.09 |
| DS-SLAM (SegNet) | 59.4 |
| DynaSLAM (Mask R-CNN) | 192.00 |
| RDS-SLAM (Mask-RCNN) | 57.5 |
| RDS-SLAM (Segnet) | 57.5 |
8.4.5. Challenges in replicating results using custom datasets
9. Future work directions
10. Conclusion
CRediT authorship contribution statement
Declaration of competing interest
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